Datenvisualisierung 4
Multi-part plots and customisations
2024-06-20
Dodged density plots
We can produce density plots mapped along a catgorical variable by using geom_half_violin() from the gghalves package.
Adding a boxplot
We can also add another geom to add more information to the plot. Let’s add a boxplot.
position_nudge()
Maybe we want to move the boxplot so that it’s not overtop of the density plots, and so that it’s not quite as wide. We can do this by setting position to position_nudge(), and width to some value smaller than .75, which is the default width.
position_jitter() for scatterplots
This is from a family of options that allow us to alter the position of geoms. For example, Abbildung 4 A and B both show the exact same data, but Abbildung 4 B includes position = position_jitter(0.2) to move overlapping points. This way we get a good idea of how many observations there were across reaction times (y-axis).
Code
df_eng |>
ggplot() +
aes(x = age_subject, y = rt_lexdec) +
geom_point() +
labs(title = "geom_point()") +
df_eng |>
ggplot() +
aes(x = age_subject, y = rt_lexdec) +
geom_point(position = position_jitter(0.2),
alpha = 0.2)+
labs(title = "geom_point(position = position_jitter(0.2))") +
plot_annotation(tag_levels = "A")
Abbildung 4: Plotting points along a categorical variable without (A) and with (B) position = position_jitter(0.2). Plot B also includes alpha = 0.2
Combining all three
If we put all of these plots together, we get a Abbildung 5.
Abbildung 5: Density, boxplot, scatterplot
Positioning errorbar plots
In the second report, you produced errorbar plots, but the errorbars were overlapping.
Abbildung 6: Overlapping errorbars
Overlapping errorbars
If we create an errorbar plot of this data, we get Abbildung 7.
Abbildung 7: Overlapping errorbar plot
position_dodge()
We can add position = position_dodge(0.2) to force the errorbars to not overlap. We’ll also adjust their width so they’re not so wide (any value lower than 0.75).
Abbildung 8: Overlapping errorbar plot
dodging all relevant geoms
But now we’ve left the points behind. We need to also dodge the points, so we add position_dodge() to geom_point(), making sure to use the same value as we did with geom_errorbar().
Abbildung 9: Overlapping errorbar plot
Customisations
What customisations do you see in the plots in Abbildung 10?
Code
fig_dens_colour <-
df_eng %>%
ggplot(aes(x = age_subject, y = rt_lexdec, )) +
geom_point(
color = "grey",
position = position_jitter(0.2),
alpha = 0.2) +
geom_half_violin(
aes(fill = age_subject)) +
geom_boxplot(
outlier.shape = NA,
aes(color = age_subject),
width = .3,
position = position_nudge(x=0.2)) +
labs(title = "Distribution of reaction times",
x = "Age group",
y = "LDT reaction time (ms)",
fill = "Age group") +
scale_color_colorblind() +
scale_fill_colorblind() +
theme_minimal() +
theme(legend.position = "none")
fig_point_colour <-
df_eng %>%
ggplot(aes(x = age_subject, y = rt_lexdec, )) +
geom_point(
aes(color = age_subject),
position = position_jitter(0.2),
alpha = 0.2) +
geom_half_violin() +
geom_boxplot(
outlier.shape = NA,
# aes(color = age_subject),
width = .3,
position = position_nudge(x=0.2)) +
labs(title = "Distribution of reaction times",
x = "Age group",
y = "LDT reaction time (ms)",
fill = "Age group") +
scale_color_colorblind() +
scale_fill_colorblind() +
theme_minimal() +
theme(legend.position = "none")
fig_default <-
sum_eng %>%
ggplot(aes(x = age_subject, y = mean,
colour = task, shape = task)) +
geom_point() +
geom_errorbar(aes(ymin=mean-sd,ymax=mean+sd))
fig_custom <-
sum_eng %>%
mutate(task = fct_recode(task,
"LDT" = "rt_lexdec",
"Naming" = "rt_naming"),
age_subject = fct_recode(age_subject,
"Young" = "young",
"Old" = "old")) |>
ggplot(aes(x = age_subject, y = mean,
colour = task, shape = task)) +
geom_point(position = position_dodge(0.3),
size = 3) +
geom_errorbar(aes(ymin=mean-sd,ymax=mean+sd),
position = position_dodge(0.3),
width = .3) +
geom_line(aes(group = task,
linetype = task),
position = position_dodge(0.3)) +
theme_minimal() +
labs(
title = "Reaction times per group and task",
x = "Age group",
y = "Reaction time (ms)",
colour = "Task",
shape = "Task",
linetype = "Task"
) +
theme(axis.title = element_text(size = 12,
face = "bold"),
plot.title = element_text(size = 14),
legend.title = element_text(face = "bold"))
Abbildung 10: Customised plots to facilitation data communication.
Default themes
Firstly, theme_minimal() was added to each plot to customise the general look. There are a variety of custom themes to try, like theme_bw() or theme_classic(). Try them out.
Abbildung 11: Preset themes
theme()
We can also control individual components of theme by adding customisations with theme(). For example we see in Abbildung 10 A the axis titles are bolded. This was achieved by adding theme(axis.title = element_text(face = "bold)), where axis.title = indicates we want to make a change to the axis titles, element_text() indicates it’s their text that we want to change, and face = "bold" indicates we want to make the text bold. The same was done for legend.title = to make the legend title bold.
